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相关概念视频

Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

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在免疫-物质相互作用中,基于机器学习的无标签巨细胞表型化.

Chawalwat Martkamjan1,2, Kornlavit Lerdsudkanung1,2, Paul Sean Tipay2,3

  • 1International School of Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.

Journal of materials chemistry. B
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概括

这项研究介绍了一种基于人工智能的成像技术,用于非侵入性巨细胞表型化,这对于改善植入物生物相容性至关重要. 该方法可以准确区分巨细胞的亚型,有助于开发更安全的生物医学植入物.

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科学领域:

  • 生物医学工程 生物医学工程
  • 免疫学 免疫学 免疫学
  • 材料科学 材料科学 材料科学

背景情况:

  • 可植入的生物医学材料需要了解巨细胞相互作用,以获得最佳的免疫相容性.
  • 巨细胞是具有塑性表型 (M1/M2) 的关键免疫调节体,但传统方法存在局限性.
  • 开发用于无标签巨细胞表型化的先进技术至关重要.

研究的目的:

  • 开发和验证使用人工智能和定量相成像 (QPI) 的高通量,无标签的巨表型化方法.
  • 评估巨细胞对不同原涂层 (I,III,IV类型) 的反应,用于生物材料应用.

主要方法:

  • 将THP-1巨细胞分化为M0,M1,M2a和M2c表型.
  • 利用定量相成像 (QPI) 来分析形态和折射率属性.
  • 采用深度学习模型 (GoogLeNet,ShuffleNet,VGG-16,ResNet-18) 来进行人工智能驱动的图像分类,ResNet-18的准确性达到90%以上.
  • 通过机器学习和细胞因子分析,评估巨细胞对原涂层的反应.

主要成果:

  • 与深度学习 (ResNet-18) 结合的QPI能够实现准确 (>90%) 的巨细胞表型.
  • 原I涂层诱导了一种促炎 (M1) 反应.
  • 原III支持平衡的M1/M2形状,而原IV促进了受控的免疫环境.

结论:

  • 人工智能驱动的QPI提供了一种非侵入性,高吞吐量方法来对巨细胞进行表征.
  • 这种方法为生物材料免疫相容性提供了宝贵的见解.
  • 这些发现可以为下一代生物医学植入物的设计提供信息,以提高生物相容性.